期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 21, 期 12, 页码 10766-10779出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2022.3187156
关键词
Over-the-air computation (AirComp); Internetof-Things (IoT) networks; Internet-of-Things (IoT) networks
资金
- Science and Technology Program of Guangzhou [202102020869]
- Guangdong Basic and Applied Basic Research Foundation [2022A1515010153, 2021A1515011645]
- Joint Funds of the National Natural Science Foundation of China and Guangdong [U2001203]
- National Natural Science Foundation of China [61871136, 61971376, 61831004]
- Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars [LR19F010002]
- Engineering and Physical Sciences Research Council [EP/P034284/1, EP/P003990/1]
- European Research Council's Advanced Fellow Grant Quant Com [789028]
This paper investigates a double-RIS-assisted AirComp system to improve computation accuracy. A pair of algorithms are proposed to optimize system parameters, and the complexity and convergence of the algorithms are analyzed. Simulation results are provided to evaluate the performance of the system.
Over-the-air computation (AirComp) has received substantial attention, given its ability to aggregate massive amounts of data from distributed wireless devices (WDs). However, the computation accuracy at the fusion center (FC) may be severely affected by receiving data corrupted by the poor channel conditions. To mitigate this issue, we consider the employment of reconfigurable intelligent surfaces (RISs) in the AirComp system considered for improving the quality of received data, and hence improve the computation accuracy. However, most previous contributions on RIS-assisted AirComp systems only employ a single RIS in the resultant single-RIS-assisted (SRIS-assisted) AirComp systems. We develop this concept further for mitigating the deleterious channel effects by conceiving a double-RIS-assisted (DRIS-assisted) AirComp system, where one of the RISs is located near the WDs and the other in the vicinity of the FC. We theoretically prove that the DRIS-assisted AirComp system outperforms its SRIS-assisted counterpart in terms of the resultant computation mean-squared-error (MSE). Furthermore, we propose a pair of algorithms for jointly optimizing the transmit power at the WDs, the receive beamforming vector at the FC, and the passive beamforming matrices at the RISs for minimizing the computational MSE. Specifically, the transmit power is updated by exploiting the Lagrange duality method, while the receive beamforming vector is optimized by utilizing the first-order optimality condition. Furthermore, a pair of techniques are developed for optimizing the passive beamforming matrices at the RISs based on semidefinite relaxation (SDR) and penalty-duality-decomposition (PDD), respectively. Both the complexity and the convergence of the proposed algorithms are analyzed. Finally, simulation results are provided for quantifying the overall performance of the resultant DRIS-assisted AirComp system.
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